from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

print_time_report()

Time report

daal4py_KMeans_short: 0h 0m 1s

daal4py_Ridge: 0h 0m 1s

KMeans_short: 0h 0m 2s

daal4py_LogisticRegression: 0h 0m 4s

daal4py_KMeans_tall: 0h 0m 8s

Ridge: 0h 0m 10s

LogisticRegression: 0h 0m 19s

KMeans_tall: 0h 0m 21s

daal4py_KNeighborsClassifier_kd_tree: 0h 0m 29s

daal4py_KNeighborsClassifier: 0h 2m 35s

KNeighborsClassifier_kd_tree: 0h 2m 41s

catboost_lossguide: 0h 5m 3s

catboost_symmetric: 0h 5m 6s

lightgbm: 0h 5m 11s

xgboost: 0h 5m 18s

HistGradientBoostingClassifier: 0h 5m 19s

KNeighborsClassifier: 0h 31m 33s

total: 1h 4m 30s

print_env_info()

Benchmark environment

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.135	0.000	5.927	0.000	1	1	NaN	NaN	0.455	0.000	0.297	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	12.171	0.040	0.000	0.012	1	1	0.708	0.721	1.801	0.027	6.757	0.103	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.177	0.001	0.000	0.177	1	1	1.000	1.000	0.082	0.001	2.174	0.031	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.126	0.000	6.353	0.000	1	100	NaN	NaN	0.438	0.000	0.287	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	20.510	0.109	0.000	0.021	1	100	0.931	0.815	1.833	0.027	11.187	0.174	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.186	0.001	0.000	0.186	1	100	1.000	1.000	0.083	0.001	2.222	0.038	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.130	0.000	6.160	0.000	-1	100	NaN	NaN	0.444	0.000	0.292	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	31.800	0.000	0.000	0.032	-1	100	0.931	0.944	1.906	0.028	16.687	0.244	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.162	0.014	0.000	0.162	-1	100	1.000	1.000	0.083	0.001	1.961	0.167	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.125	0.000	6.402	0.000	-1	5	NaN	NaN	0.440	0.000	0.284	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	31.564	0.000	0.000	0.032	-1	5	0.801	0.721	1.828	0.035	17.263	0.332	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.159	0.013	0.000	0.159	-1	5	1.000	1.000	0.082	0.001	1.955	0.162	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.123	0.000	6.520	0.000	-1	1	NaN	NaN	0.438	0.000	0.280	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	23.931	0.251	0.000	0.024	-1	1	0.708	0.815	1.882	0.042	12.716	0.312	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.167	0.017	0.000	0.167	-1	1	1.000	1.000	0.083	0.002	2.012	0.208	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.124	0.000	6.472	0.000	1	5	NaN	NaN	0.443	0.000	0.279	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	20.740	0.055	0.000	0.021	1	5	0.801	0.944	1.943	0.017	10.672	0.096	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.187	0.002	0.000	0.187	1	5	1.000	1.000	0.086	0.008	2.182	0.200	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.058	0.000	0.276	0.000	1	1	NaN	NaN	0.094	0.000	0.620	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	9.737	0.051	0.000	0.010	1	1	0.966	0.977	0.296	0.007	32.861	0.748	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.015	0.000	0.000	0.015	1	1	1.000	1.000	0.006	0.000	2.652	0.095	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.058	0.000	0.276	0.000	1	100	NaN	NaN	0.097	0.000	0.598	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.897	0.090	0.000	0.020	1	100	0.980	0.983	0.281	0.014	70.915	3.541	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.021	0.000	0.000	0.021	1	100	1.000	1.000	0.005	0.000	4.037	0.183	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.058	0.000	0.278	0.000	-1	100	NaN	NaN	0.092	0.000	0.626	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	30.239	0.000	0.000	0.030	-1	100	0.980	0.983	0.323	0.006	93.577	1.668	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.024	0.001	0.000	0.024	-1	100	1.000	1.000	0.006	0.000	4.283	0.223	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.309	0.000	-1	5	NaN	NaN	0.093	0.000	0.557	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	30.804	0.000	0.000	0.031	-1	5	0.977	0.977	0.277	0.010	111.379	3.870	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.025	0.002	0.000	0.025	-1	5	1.000	1.000	0.006	0.000	4.344	0.408	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.058	0.000	0.276	0.000	-1	1	NaN	NaN	0.095	0.000	0.612	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.017	0.174	0.000	0.021	-1	1	0.966	0.983	0.284	0.002	74.094	0.869	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.019	0.002	0.000	0.019	-1	1	1.000	1.000	0.006	0.000	3.363	0.440	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.305	0.000	1	5	NaN	NaN	0.094	0.000	0.556	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	18.190	0.159	0.000	0.018	1	5	0.977	0.983	0.342	0.007	53.195	1.223	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.000	0.000	0.020	1	5	1.000	1.000	0.006	0.000	3.661	0.220	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.088	0.000	0.026	0.000	-1	1	NaN	NaN	0.737	0.000	4.188	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.438	0.008	0.000	0.000	-1	1	0.970	0.974	0.605	0.009	0.724	0.017	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.001	0.000	2.325	0.687	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.339	0.000	0.024	0.000	1	1	NaN	NaN	0.703	0.000	4.750	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.747	0.005	0.000	0.001	1	1	0.970	0.980	0.194	0.002	3.856	0.044	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	2.642	1.123	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.190	0.000	0.025	0.000	1	5	NaN	NaN	0.743	0.000	4.291	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.462	0.014	0.000	0.001	1	5	0.979	0.974	0.597	0.017	2.449	0.075	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	1	5	1.000	1.000	0.001	0.000	1.703	0.660	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.160	0.000	0.025	0.000	-1	100	NaN	NaN	0.710	0.000	4.453	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.759	0.017	0.000	0.003	-1	100	0.978	0.968	0.110	0.001	25.172	0.280	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.009	0.001	0.000	0.009	-1	100	1.000	1.000	0.000	0.000	26.653	11.287	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.271	0.000	0.024	0.000	-1	5	NaN	NaN	0.698	0.000	4.684	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.847	0.007	0.000	0.001	-1	5	0.979	0.980	0.196	0.004	4.319	0.097	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.000	0.000	0.004	-1	5	1.000	1.000	0.000	0.000	9.259	3.825	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.309	0.000	0.024	0.000	1	100	NaN	NaN	0.698	0.000	4.737	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.814	0.051	0.000	0.005	1	100	0.978	0.968	0.108	0.002	44.661	1.092	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.007	0.000	0.000	0.007	1	100	1.000	1.000	0.000	0.000	19.393	7.225	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.856	0.000	0.019	0.000	-1	1	NaN	NaN	0.487	0.000	1.759	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.000	0.001	0.000	-1	1	0.970	0.978	0.007	0.000	3.519	0.155	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	17.148	12.433	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.860	0.000	0.019	0.000	1	1	NaN	NaN	0.490	0.000	1.756	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.000	0.001	0.000	1	1	0.970	0.980	0.001	0.000	21.805	6.063	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.085	3.896	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.876	0.000	0.018	0.000	1	5	NaN	NaN	0.485	0.000	1.807	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.000	0.001	0.000	1	5	0.988	0.978	0.007	0.000	3.608	0.174	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	4.565	3.386	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.862	0.000	0.019	0.000	-1	100	NaN	NaN	0.476	0.000	1.812	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.046	0.000	0.000	0.000	-1	100	0.988	0.977	0.001	0.000	59.761	20.083	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.000	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	22.248	17.286	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.875	0.000	0.018	0.000	-1	5	NaN	NaN	0.470	0.000	1.862	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.028	0.001	0.001	0.000	-1	5	0.988	0.980	0.001	0.000	25.002	6.829	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.000	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	24.486	18.080	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.830	0.000	0.019	0.000	1	100	NaN	NaN	0.465	0.000	1.784	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.055	0.001	0.000	0.000	1	100	0.988	0.977	0.001	0.000	73.149	26.209	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.803	4.528	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.636	0.0	0.755	0.000	k-means++	NaN	30	NaN	0.435	0.0	1.462	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.370	0.000	k-means++	0.001	30	0.001	0.000	0.0	8.610	5.280	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.682	8.207	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.495	0.0	0.969	0.000	random	NaN	30	NaN	0.439	0.0	1.128	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.375	0.000	random	0.001	30	0.001	0.000	0.0	8.825	5.491	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.000	0.0	10.749	8.054	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.092	0.0	3.940	0.000	k-means++	NaN	30	NaN	2.768	0.0	2.201	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.068	0.000	k-means++	0.002	30	0.002	0.000	0.0	5.613	2.555	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.019	0.001	k-means++	1.000	30	1.000	0.000	0.0	9.440	6.037	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	5.824	0.0	4.121	0.000	random	NaN	30	NaN	2.928	0.0	1.989	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	14.322	0.000	random	0.001	30	0.002	0.000	0.0	6.191	2.588	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.019	0.001	random	1.000	30	1.000	0.000	0.0	9.553	6.714	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.079	0.0	0.040	0.000	random	NaN	20	NaN	0.035	0.0	2.286	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.185	0.000	random	0.001	20	-0.000	0.001	0.0	2.488	0.364	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	random	1.000	20	1.000	0.000	0.0	9.846	6.722	See	See
3	KMeans_short	fit	10000	10000	2	20	0.250	0.0	0.013	0.000	k-means++	NaN	20	NaN	0.088	0.0	2.830	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.190	0.000	k-means++	0.001	20	-0.001	0.001	0.0	2.442	0.471	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	9.137	6.180	See	See
6	KMeans_short	fit	10000	10000	100	20	0.220	0.0	0.726	0.000	random	NaN	20	NaN	0.146	0.0	1.513	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.0	6.296	0.000	random	0.312	20	0.227	0.001	0.0	1.955	0.306	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.010	0.002	random	1.000	20	1.000	0.000	0.0	9.041	5.070	See	See
9	KMeans_short	fit	10000	10000	100	20	0.631	0.0	0.254	0.000	k-means++	NaN	20	NaN	0.357	0.0	1.769	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.0	6.056	0.000	k-means++	0.296	20	0.318	0.001	0.0	2.124	0.274	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	7.710	4.164	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	11.068	0.0	[-0.10660473]	0.000	NaN	NaN	NaN	NaN	NaN	1.903	0.0	5.816	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[52.60662541]	0.000	NaN	NaN	NaN	NaN	0.554	0.000	0.0	0.815	0.368	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.2273987]	0.000	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.420	0.379	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	0.783	0.0	[2.65793314]	0.001	NaN	NaN	NaN	NaN	NaN	0.752	0.0	1.041	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.002	0.0	[116.23383162]	0.000	NaN	NaN	NaN	NaN	0.320	0.003	0.0	0.544	0.079	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[22.50074103]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.118	0.080	See	See

Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.181	0.0	0.441	0.0	NaN	NaN	NaN	0.181	0.0	0.999	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.010	0.0	7.655	0.0	NaN	NaN	0.086	0.017	0.0	0.600	0.018	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	1.229	0.0	NaN	NaN	NaN	0.000	0.0	0.633	0.629	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.343	0.0	0.596	0.0	NaN	NaN	NaN	0.243	0.0	5.526	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	4.220	0.0	NaN	NaN	1.000	0.000	0.0	0.805	0.597	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.012	0.0	NaN	NaN	NaN	0.000	0.0	0.712	0.767	See	See

Gradient boosting: randomized HPO benchmarks

reporting_hpo = ReportingHpo(config="config.yml")
reporting_hpo.run()

Raw results

Smoothed HPO Curves